Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence

• URL: http://arxiv.org/abs/2402.08749v1
• Date: Tue, 13 Feb 2024 19:36:23 GMT
• Title: Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence
• Authors: Marina Manso Jimeno, Keerthi Sravan Ravi, Maggie Fung, John Thomas Vaughan, Jr., Sairam Geethanath
• Abstract summary: This study demonstrates a deep learning model to detect rigid motion in T1-weighted brain images. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. This model is part of the ArtifactID tool, aimed at inline automatic detection of Gibbs ringing, wrap-around, and motion artifacts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning model to detect rigid motion in T1-weighted brain images. We leveraged a 2D CNN for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is part of the ArtifactID tool, aimed at inline automatic detection of Gibbs ringing, wrap-around, and motion artifacts. This tool automates part of the time-consuming QA process and augments expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.

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